Federated Learning Over the Industrial Internet of Things: A Joint
Optimization of Edge Association and Resource Allocation
Abstract
Combination of the industrial Internet of Things (IIoT) and federated
learning (FL) is deemed as a promising solution to realizing Industry
4.0 and beyond. In this paper, we focus on a hierarchical collaborative
FL architecture over the IIoT systems, where the three-layer
architectural design is conceived for supporting the training process.
To effectively balance among the learning speed, energy consumption, and
packet error rate for edge aggregation with regard to the participating
IIoT devices, a weighted learning utility function is developed from the
perspective of the fusing multiple performance metrics. An optimization
problem is formulated to maximize the weighted learning utility by
jointly optimizing the edge association as well as the allocations of
resource block (RB), computation capacity, and transmit power of each
IIoT device, under the practical constraints of the FL training process.
The resulting problem is a non-convex and mixed integer optimization
problem, and consequently it is difficult to solve. By resorting to the
block coordinate descent method, we propose an overall alternating
optimization algorithm to solve this problem in an iterative way.
Specifically, in each iteration, for given transmit power and
computation capacity, the sub-problem of joint RB assignment and edge
association is transformed to a three-uniform weighted hypergraph model,
which is solved by the local search-based three-dimensional hypergraph
matching algorithm. Second, given RB assignment, edge association, and
computation capacity, we employ the successive convex approximation
method to tackle the sub-problem for optimizing the transmit power by
turning it into a convex approximation problem. After the proposed
alternating optimization algorithm converges to a tolerance threshold, a
locally optimal solution of the original problem can be found. Numerical
results reveal that our proposed joint optimization scheme can increase
the system-wide learning utility and achieve significant performance
gains over the four benchmark schemes.